Comparative model-building of the mammalian serine proteases

Proteins have been classified into families based upon sequence homology. An accurate, systematic comparative model-building procedure for a homologous family of proteins would be very valuable scientifically. This paper presents such a procedure and applies it to the mammalian serine proteases, which are ubiquitous and involved in many important biological functions. Eleven proteins of this family are considered here, including a variety of blood serum, intestinal and pancreatic proteins as well as a closely related bacterial enzyme.The modeling method capitalizes upon the availability of three experimentally determined structures for mammalian serine proteases. These structures show that the molecule is divided into structurally conserved regions, which contain the strong sequence homology, and structurally variable regions, which include all the additions and deletions. We show that by applying this structural distinction to new sequences, erroneous alignments of the sequences are greatly minimized.For each aligned new sequence, the structurally conserved regions can be constructed from any of the known structures. In examining the variable regions, we have found that a variable region that has the same length and residue character in two different known structures usually has the same conformation in both. Thus, when the eight structurally unknown proteins are modeled, most of the variable regions can be constructed directly from the known structures. A minority of the variable regions require more sophisticated analysis to evaluate the relative merits of a small number of possible conformations. Only a very few are so different that modeling by homology is entirely ruled out. We demonstrate, therefore, that by this modeling procedure, the maximum of each of these mammalian serine proteases is constructed directly from the experimentally determined structures and the necessity to build from intuition or from energy considerations is greatly reduced.